from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.000	7.198	0.0	-1	1	0.049	0.000	0.229	0.229	See	See
3	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.000	7.108	0.0	-1	5	0.046	0.001	0.230	0.230	See	See
6	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.012	0.001	6.503	0.0	1	100	0.046	0.000	0.268	0.268	See	See
9	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.000	7.193	0.0	-1	100	0.047	0.000	0.236	0.236	See	See
12	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.000	7.189	0.0	1	5	0.047	0.000	0.242	0.242	See	See
15	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	100	0.011	0.000	7.167	0.0	1	1	0.049	0.000	0.239	0.239	See	See
18	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.000	0.364	0.0	-1	1	0.008	0.000	0.534	0.534	See	See
21	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.000	0.358	0.0	-1	5	0.008	0.000	0.541	0.541	See	See
24	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.000	0.374	0.0	1	100	0.009	0.000	0.513	0.513	See	See
27	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.000	0.375	0.0	-1	100	0.009	0.001	0.484	0.486	See	See
30	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.000	0.373	0.0	1	5	0.009	0.000	0.493	0.493	See	See
33	KNeighborsClassifier_brute_force	sklearn	fit	100000	100000	2	0.004	0.000	0.373	0.0	1	1	0.008	0.000	0.490	0.490	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	1.959	0.055	0.000	0.002	-1	1	0.181	0.005	11.059	11.059	See	See
2	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.023	0.004	0.000	0.023	-1	1	0.009	0.000	2.474	2.474	See	See
4	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	3.111	0.088	0.000	0.003	-1	5	0.177	0.000	17.199	17.205	See	See
5	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.026	0.003	0.000	0.026	-1	5	0.009	0.000	3.055	3.057	See	See
7	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	2.100	0.004	0.000	0.002	1	100	0.211	0.001	11.857	11.857	See	See
8	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.023	0.005	0.000	0.023	1	100	0.009	0.000	2.593	2.594	See	See
10	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	2.887	0.043	0.000	0.003	-1	100	0.212	0.001	13.634	13.634	See	See
11	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.025	0.002	0.000	0.025	-1	100	0.009	0.000	2.824	2.825	See	See
13	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	2.086	0.002	0.000	0.002	1	5	0.177	0.000	9.869	9.869	See	See
14	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.020	0.000	0.000	0.020	1	5	0.009	0.000	2.280	2.281	See	See
16	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	100	1.183	0.005	0.001	0.001	1	1	0.177	0.000	6.681	6.681	See	See
17	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	100	0.019	0.000	0.000	0.019	1	1	0.009	0.000	2.182	2.183	See	See
19	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	1.827	0.015	0.000	0.002	-1	1	0.026	0.000	70.460	70.488	See	See
20	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.008	0.003	0.000	0.008	-1	1	0.001	0.000	11.031	11.174	See	See
22	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.814	0.024	0.000	0.003	-1	5	0.027	0.001	109.050	109.056	See	See
23	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.009	0.001	0.000	0.009	-1	5	0.001	0.000	12.067	12.265	See	See
25	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.071	0.004	0.000	0.002	1	100	0.061	0.000	76.099	76.164	See	See
26	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.034	3.226	See	See
28	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.827	0.016	0.000	0.003	-1	100	0.063	0.004	45.037	45.108	See	See
29	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.008	0.002	0.000	0.008	-1	100	0.001	0.000	10.241	10.403	See	See
31	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	2.055	0.004	0.000	0.002	1	5	0.027	0.000	33.908	33.909	See	See
32	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	3.520	3.566	See	See
34	KNeighborsClassifier_brute_force	sklearn	predict	100000	1000	2	1.086	0.002	0.000	0.001	1	1	0.026	0.001	40.318	40.319	See	See
35	KNeighborsClassifier_brute_force	sklearn	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.612	2.667	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.196	0.138	0.025	0.0	1	5	0.721	0.005	4.220	4.221	See	See
3	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.635	0.039	0.022	0.0	-1	5	0.715	0.016	4.913	4.914	See	See
6	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.569	0.019	0.022	0.0	-1	1	0.757	0.019	4.933	4.934	See	See
9	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.682	0.034	0.022	0.0	1	1	0.724	0.013	5.147	5.148	See	See
12	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.648	0.035	0.022	0.0	1	100	0.740	0.015	5.058	5.058	See	See
15	KNeighborsClassifier_kd_tree	sklearn	fit	1000000	1000000	10	3.829	0.046	0.021	0.0	-1	100	0.728	0.014	5.258	5.259	See	See
18	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	1	5	0.001	0.000	0.158	0.166	See	See
21	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	5	0.001	0.000	0.394	0.473	See	See
24	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	1	0.003	0.001	0.614	0.622	See	See
27	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	1	1	0.001	0.000	0.562	0.570	See	See
30	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	1	100	0.001	0.001	0.612	0.619	See	See
33	KNeighborsClassifier_kd_tree	sklearn	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	100	0.001	0.000	0.616	0.623	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	1.597	0.014	0.000	0.002	1	5	0.207	0.006	13.482	13.484	See	See
2	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.961	6.631	See	See
4	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	0.924	0.009	0.000	0.001	-1	5	0.197	0.001	1.561	1.561	See	See
5	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	5.215	5.704	See	See
7	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	0.499	0.008	0.000	0.000	-1	1	0.118	0.002	4.202	4.207	See	See
8	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	11.199	12.468	See	See
10	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	0.833	0.005	0.000	0.001	1	1	0.119	0.005	4.239	4.239	See	See
11	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	4.202	4.722	See	See
13	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	5.643	0.082	0.000	0.006	1	100	0.592	0.008	27.285	27.297	See	See
14	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	9.963	10.848	See	See
16	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1000	10	3.094	0.054	0.000	0.003	-1	100	0.605	0.011	5.109	5.110	See	See
17	KNeighborsClassifier_kd_tree	sklearn	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	8.303	9.065	See	See
19	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	5	0.001	0.000	39.989	41.368	See	See
20	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.052	6.491	See	See
22	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.022	0.001	0.001	0.000	-1	5	0.001	0.000	4.660	4.672	See	See
23	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	16.596	21.262	See	See
25	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.021	0.001	0.001	0.000	-1	1	0.000	0.000	42.266	43.569	See	See
26	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	17.162	21.557	See	See
28	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.018	0.000	0.001	0.000	1	1	0.000	0.000	25.215	25.686	See	See
29	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.442	7.189	See	See
31	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.033	0.000	0.000	0.000	1	100	0.005	0.000	43.573	44.253	See	See
32	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.371	7.032	See	See
34	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1000	2	0.033	0.000	0.000	0.000	-1	100	0.005	0.000	7.296	7.308	See	See
35	KNeighborsClassifier_kd_tree	sklearn	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	16.767	21.248	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	sklearn	fit	1000000	1000000	2	0.558	0.004	30	0.029	0.0	k-means++	0.401	0.019	1.391	1.392	See	See
3	KMeans_tall	sklearn	fit	1000000	1000000	2	0.485	0.003	30	0.033	0.0	random	0.369	0.013	1.315	1.316	See	See
6	KMeans_tall	sklearn	fit	1000000	1000000	100	5.832	0.045	30	0.137	0.0	k-means++	2.987	0.143	1.952	1.955	See	See
9	KMeans_tall	sklearn	fit	1000000	1000000	100	5.555	0.039	30	0.144	0.0	random	2.893	0.019	1.920	1.920	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	sklearn	predict	1000000	1000	2	0.002	0.0	30	0.010	0.000	k-means++	0.0	0.0	10.636	13.094	See	See
2	KMeans_tall	sklearn	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	k-means++	0.0	0.0	10.270	13.511	See	See
4	KMeans_tall	sklearn	predict	1000000	1000	2	0.001	0.0	30	0.012	0.000	random	0.0	0.0	8.489	10.159	See	See
5	KMeans_tall	sklearn	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	random	0.0	0.0	10.842	13.755	See	See
7	KMeans_tall	sklearn	predict	1000000	1000	100	0.002	0.0	30	0.481	0.000	k-means++	0.0	0.0	5.403	5.992	See	See
8	KMeans_tall	sklearn	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	k-means++	0.0	0.0	10.529	13.211	See	See
10	KMeans_tall	sklearn	predict	1000000	1000	100	0.002	0.0	30	0.494	0.000	random	0.0	0.0	5.837	6.531	See	See
11	KMeans_tall	sklearn	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	random	0.0	0.0	10.418	13.319	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 3 predictions (37.5%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	library	diff_adjusted_rand_scores	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
4	KMeans_short	sklearn	0.001361	predict	10000	1000	2	0.001857	0.000171	20	0.008616	0.000002	full	k-means++	20	300	1	1.000000e-16	0.000744	0.000494	0.000095	-0.000617	3.762837	3.832135
7	KMeans_short	sklearn	0.002111	predict	10000	1000	100	0.002516	0.000255	20	0.317912	0.000003	full	random	20	300	1	1.000000e-16	0.340206	0.001026	0.000151	0.342317	2.452647	2.479219
10	KMeans_short	sklearn	0.016992	predict	10000	1000	100	0.002428	0.000273	20	0.329554	0.000002	full	k-means++	20	300	1	1.000000e-16	0.321348	0.001018	0.000147	0.338339	2.383608	2.408166

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	sklearn	fit	10000	10000	2	0.077	0.001	20	0.002	0.0	random	0.026	0.001	3.016	3.017	See	See
3	KMeans_short	sklearn	fit	10000	10000	2	0.217	0.002	20	0.001	0.0	k-means++	0.080	0.001	2.698	2.698	See	See
6	KMeans_short	sklearn	fit	10000	10000	100	0.199	0.003	20	0.040	0.0	random	0.105	0.001	1.906	1.906	See	See
9	KMeans_short	sklearn	fit	10000	10000	100	0.563	0.016	20	0.014	0.0	k-means++	0.299	0.001	1.885	1.885	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	sklearn	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.001	0.0	3.698	3.774	See	See
2	KMeans_short	sklearn	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	10.520	12.830	See	See
4	KMeans_short	sklearn	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	3.763	3.832	See	See
5	KMeans_short	sklearn	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	10.133	12.328	See	See
7	KMeans_short	sklearn	predict	10000	1000	100	0.003	0.0	20	0.318	0.000	random	0.001	0.0	2.453	2.479	See	See
8	KMeans_short	sklearn	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	8.424	9.800	See	See
10	KMeans_short	sklearn	predict	10000	1000	100	0.002	0.0	20	0.330	0.000	k-means++	0.001	0.0	2.384	2.408	See	See
11	KMeans_short	sklearn	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	8.552	9.972	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	sklearn	fit	1000000	1000000	100	11.222	0.155	[20]	0.071	0.000	2.036	0.076	5.512	5.516	See	See
3	LogisticRegression	sklearn	fit	1000	1000	10000	0.808	0.009	[27]	0.099	0.001	0.976	0.034	0.828	0.828	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	sklearn	predict	1000000	1000	100	0.000	0.0	[20]	2.471	0.0	0.000	0.0	0.940	1.027	See	See
2	LogisticRegression	sklearn	predict	1000000	1	100	0.000	0.0	[20]	0.012	0.0	0.000	0.0	0.388	0.451	See	See
4	LogisticRegression	sklearn	predict	1000	100	10000	0.002	0.0	[27]	4.994	0.0	0.004	0.0	0.442	0.444	See	See
5	LogisticRegression	sklearn	predict	1000	1	10000	0.000	0.0	[27]	0.821	0.0	0.001	0.0	0.137	0.143	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Raw results

fit

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	sklearn	fit	1000	1000	10000	0.171	0.010	0.467	0.0	0.180	0.001	0.950	0.950	See	See
3	Ridge	sklearn	fit	1000000	1000000	100	1.090	0.048	0.734	0.0	0.225	0.007	4.842	4.844	See	See

predict

	estimator	library	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	sklearn	predict	1000	1000	10000	0.012	0.001	6.452	0.0	0.019	0.0	0.662	0.662	See	See
2	Ridge	sklearn	predict	1000	1	10000	0.000	0.000	1.102	0.0	0.000	0.0	0.669	0.800	See	See
4	Ridge	sklearn	predict	1000000	1000	100	0.000	0.000	5.311	0.0	0.000	0.0	0.658	0.756	See	See
5	Ridge	sklearn	predict	1000000	1	100	0.000	0.000	0.014	0.0	0.000	0.0	0.626	0.771	See	See